U.S. patent application number 17/432734 was filed with the patent office on 2022-05-05 for method for analyzing differentiation of metabolites in urine sample between different groups.
This patent application is currently assigned to Korea University Research and Business Foundation. The applicant listed for this patent is Korea University Research and Business Foundation. Invention is credited to Joong Kyong AHN, Hoon Suk CHA, Jung Yeon KIM, Kyoung Heon KIM.
Application Number | 20220137012 17/432734 |
Document ID | / |
Family ID | 1000006138801 |
Filed Date | 2022-05-05 |
United States Patent
Application |
20220137012 |
Kind Code |
A1 |
KIM; Kyoung Heon ; et
al. |
May 5, 2022 |
METHOD FOR ANALYZING DIFFERENTIATION OF METABOLITES IN URINE SAMPLE
BETWEEN DIFFERENT GROUPS
Abstract
The present invention relates to a method for metabolite
sampling and analysis for reproducibly sampling as many metabolites
as possible in a urine sample without changing to metabolites. The
method has effects of presenting a biomarker detection method
according to the sex or the like, by establishing optimal
conditions for metabolite sampling in urine samples and presenting
a metabolite comparison analysis method between different groups on
the basis of the optimal conditions.
Inventors: |
KIM; Kyoung Heon; (Seoul,
KR) ; CHA; Hoon Suk; (Seoul, KR) ; AHN; Joong
Kyong; (Seoul, KR) ; KIM; Jung Yeon; (Seoul,
KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Korea University Research and Business Foundation |
Seoul |
|
KR |
|
|
Assignee: |
Korea University Research and
Business Foundation
Seoul
KR
|
Family ID: |
1000006138801 |
Appl. No.: |
17/432734 |
Filed: |
February 21, 2020 |
PCT Filed: |
February 21, 2020 |
PCT NO: |
PCT/KR2020/002542 |
371 Date: |
October 25, 2021 |
Current U.S.
Class: |
702/19 |
Current CPC
Class: |
G01N 30/8637 20130101;
G01N 2030/862 20130101; G01N 2030/8813 20130101; G01N 30/7206
20130101; G01N 30/14 20130101; G01N 30/861 20130101 |
International
Class: |
G01N 30/72 20060101
G01N030/72; G01N 30/86 20060101 G01N030/86; G01N 30/14 20060101
G01N030/14 |
Foreign Application Data
Date |
Code |
Application Number |
Feb 22, 2019 |
KR |
10-2019-0021461 |
Claims
1. A gender discrimination kit, comprising a quantitative device
for one or more urine metabolites selected from the group
consisting of succinate, fumarate, asparagines dihydrate, palmitic
acid, .beta.-alanine, L-cysteine, lactate, tyrosine, glycine and
stearic acid.
2. The kit according to claim 1, wherein the quantitative device is
a gas chromatography/time-of-flight mass spectrometry (GC/TOF MS)
analyzer.
3. The kit according to claim 1, wherein, in the case of males,
fumarate, asparagines dihydrate, .beta.-alanine, L-cysteine and
tyrosine among the above metabolites tend to increase while
succinate, palmitic acid, lactate, stearic acid and glycine have a
decreasing tendency.
4. The kit according to claim 1, wherein, in the case of females,
succinate, palmitic acid, lactate, stearic acid and glycine among
the above metabolites tend to increase while fumarate, asparagines
dihydrate, .beta.-alanine, L-cysteine and tyrosine have a
decreasing tendency.
5. A method for analysis of metabolite differentiation between
different groups in urine samples, comprising: a metabolite
sampling step that extracts metabolites from urine using pure
methanol or a mixed solvent of formic acid and methanol without
urease treatment of the urine.
6. The method according to claim 5, further comprising: analyzing
the extracted metabolites by means of the GC/TOF MS analyzer;
converting the GC/TOF MS analysis result into a numerical value
capable of statistically processed; and statistically verifying
discrimination between different groups using the converted
value.
7. The method according to claim 5, wherein the converting step of
the GC/TOF MS analysis result into a numerical value capable of
statistically processing includes dividing a total analysis time by
unit time intervals, and determining the largest one of an area or
height of chromatogram peaks displayed during the unit time as a
representative value for the unit time.
8. The method according to claim 5, wherein the statistical
verifying step of discrimination between two biological sample
groups using the converted values includes conducting partial least
squares discriminant analysis (PLS-DA) so as to analyze and verify
metabolite biomarkers that show a significant difference between
these two biological sample groups.
9. The method according to claim 8, wherein a positive loading
value of the partial least squares discriminant analysis (PLS-DA)
indicates an increasing tendency of metabolite biomarkers, while a
negative loading value indicates a decreasing tendency of
metabolite biomarkers.
10. The method according to claim 8, wherein the metabolite
biomarkers consist of succinate, fumarate, asparagines dihydrate,
palmitic acid, .beta.-alanine, L-cysteine, lactate, tyrosine,
glycine and stearic acid.
11. The method according to claim 8, wherein the metabolite
biomarkers discriminate gender.
Description
TECHNICAL FIELD
[0001] The present invention relates to a method for analysis of
differences between different groups in a urine sample.
BACKGROUND ART
[0002] Urine is a biological sample most useful for health
examination. A urine sample can be conveniently and non-invasively
collected and typically contains a lot of various metabolites, so
that it can be routinely used for disease diagnosis. Diseases such
as diabetes, gout, proteinuria, and specific physiological changes
such as pregnancy may change the secretion of metabolites in the
body and a constitutional composition of metabolites contained in
urine. Therefore, studies to find metabolites in urine specifically
altered due to disease and physiological variation and to quantify
the same so as to propose biomarkers have been extensively executed
for a long time. As such, the study of changes in metabolites due
to varied specific states is called metabolomics.
[0003] With regard to metabolomic research, it is very important to
prevent the change of metabolites in a sample and reproducibly
extract as many substances as possible without alteration. In the
case of urine metabolomics, a standardized urine metabolite
extraction method has been proposed in Nature Protocol (Chan E C et
al., 2011, Nat. Protoc. Vol. 6, pp 1483-1499). However, this
extraction method is not based on experimental studies and cannot
be an optimal urine metabolite extraction method because it refers
to and summarizes only the existing methods that have been used
previously. The standardized urine metabolite extraction method
adopts urease treatment to remove urea in urine, and then conducts
protein precipitation and metabolite extraction by administering
methanol. However, since urease treatment includes reaction at
37.degree. C. for 1 hour, the metabolites in urine may be modified
by activity of enzymes or the like in urine, which in turn possibly
deteriorates the ability to discover biomarkers in urine
metabolomic studies to discover biomarkers for diagnosis of
diseases. In addition, pure methanol has not been compared to and
analyzed with other extraction solvents in terms of extraction
efficiency and reproducibility, and may not be determined as an
optimal extraction solvent. Therefore, it is required to study
effects of the urease treatment on the existing standardization
method while comparing and analyzing different extraction solvents,
and therefore, to suggest a new and optimal extraction method
capable of reproducibly extracting metabolites in original states
contained in a urine sample as much as possible without
modification thereof.
DISCLOSURE
Technical Problem
[0004] In order to extract metabolites in a urine sample in as
large amounts as possible without modification thereof, the present
inventors have established a urine metabolite extraction method
using optimum extraction solvents without urease treatment and an
analysis method of metabolites between different groups (e.g., sex,
disease, etc.) based on the above metabolite extraction method,
thereby completing the present invention.
[0005] Accordingly, it is an object of the present invention to
provide a kit for discriminating sex (gender) by extracting
metabolites from a urine sample.
[0006] Another object of the present invention is to provide a
method for analyzing differences of metabolites between different
groups in urine samples.
Technical Solution
[0007] The present invention may provide a gender discrimination
kit provided with a quantification device for one or more
metabolites selected from the group consisting of succinate,
fumarate, asparagine dihydrate, palmitic acid, .beta.-alanine,
L-cysteine, lactate, tyrosine, glycine and stearic acid.
[0008] Further, the present invention may provide,
[0009] a method for analyzing differences of metabolites between
different groups in urine samples, including:
[0010] sampling a metabolite by extracting the metabolite with
methanol or a solvent mixture of formic acid and methanol without
urease treatment.
Advantageous Effects
[0011] The present invention proposes an optimized extraction
method of metabolites in a urine sample through non-urease
treatment and comparison of extraction efficiency and extraction
reproducibility between various extraction solvents in order to
reproducibly extract sample as much of the metabolites in the urine
as possible without change thereof. Further, a method for
comparative analysis of metabolites between different groups based
on the above extraction method is presented, thereby suggesting a
method for detection of biomarkers such as gender, disease,
etc.
[0012] The present invention is expected to be useful in various
pathology and biomarker presentation studies through metabolite
analysis of urine samples.
DESCRIPTION OF DRAWINGS
[0013] FIG. 1 shows metabolite profiles (A: score plot, B: loading
plot) between a stationary culture group (UI) at 37.degree. C. for
1 hour with urease treatment using PLS-DA, another stationary
culture group (WI) at 37.degree. C. for 1 hour with non-urease
treatment, and a non-stationary culture group (DE) with non-urease
treatment.
[0014] FIG. 2 shows metabolite profiles (A: score plot, B: loading
plot) between males (DE Male) and females (De-Female) in the
non-stationary culture group (DE) with non-urease treatment using
PLS-DA.
[0015] FIG. 3 illustrates comparison of amounts of 10 metabolites
that distinguish males and females in a box plot.
[0016] FIG. 4 shows comparison box plots of metabolite extraction
rates from urine on the basis of: pure methanol (MeOH); pure
ethanol (EtOH); a mixture of acetonitrile:water (50 ACN; 1:1, v/v);
and a mixture of water:2-propanol:methanol (WiPM; 2:2:5, v/v/v);
and a mixture of formic acid:methanol (AM; 0.125:99.875, v/v).
[0017] FIG. 5 shows comparison box plots of variation coefficients
(% CV) upon metabolite extraction from urine on the basis of: pure
methanol (MeOH); pure ethanol (EtOH); a mixture of
acetonitrile:water (50 ACN; 1:1, v/v); and a mixture of
water:2-propanol:methanol (WiPM; 2:2:5, v/v/v); and a mixture of
formic acid:methanol (AM; 0.125:99.875, v/v).
[0018] FIG. 6 shows comparison box plots (A) and photographs (B) of
protein precipitations rates upon metabolite extraction from urine
on the basis of: pure methanol (MeOH); pure ethanol (EtOH); a
mixture of acetonitrile:water (50 ACN; 1:1, v/v); and a mixture of
water:2-propanol:methanol (WiPM; 2:2:5, v/v/v); and a mixture of
formic acid:methanol (AM; 0.125:99.875, v/v).
BEST MODE
[0019] The present invention relates to a method for processing a
urine sample for analysis of metabolites in urine.
[0020] According to an embodiment of the present invention, in
order to reproducibly extract metabolites as much as possible in a
urine sample without changes thereof, the metabolites may be
directly extracted from the urine sample without urease
treatment.
[0021] Further, according to another embodiment of the present
invention, in order to propose a research method for distinguishing
different groups based on metabolites of the urine sample and for
finding biomarkers, different groups are compared and analyzed
based on the metabolites extracted from the urine sample without
urease treatment.
[0022] According to a further embodiment of the present invention,
as large amounts as possible of the metabolites in urine may be
reproducibly extracted, wherein pure methanol or a mixed solvent of
formic acid and methanol may be used as an extraction solvent
capable of extracting as large amounts of metabolites as possible
in urine and properly precipitating proteins.
[0023] The present inventors have conducted extraction of
metabolites using pure methanol or a mixed solvent of formic acid
and methanol without urease treatment in order to find a biomarker
that confirms discrimination between two biological sample groups
in the urine sample, and comparative analysis of differences in
metabolite profiles through GC/TOF/MS according to gender and
pr-treatment methods of urine metabolites, followed by studies to
discover desired biomarkers to distinguish gender using the above
differences based on metabolites.
[0024] As a result, 107 and/or 113 metabolites including amines,
amino acids, sugars and sugar alcohols, fatty acids, phosphoric
acids, organic acids, and the like were identified.
[0025] When comparing the biological samples from urine samples
that were obtained different pre-treatment methods, a clear
difference in metabolite profiles according to different
pre-treatment methods by PLS-DA was confirmed (FIG. 1), and a
difference in metabolite profiles in relation to gender was also
clearly confirmed (FIG. 2).
[0026] Thereamong, in regard to gender discrimination models, top
10 metabolites were selected based on VIP value of PLS-DA model for
each metabolite, which may be chosen as new biomarker candidates
for gender discrimination (Table 4).
[0027] Therefore, the present invention may include a kit for
gender identification which includes a quantification device for
one or more metabolites selected from the group consisting of
succinate, fumarate, asparagine dihydrate, palmitic acid,
beta-alanine, L-cysteine, lactate, tyrosine, glycine and stearic
acid.
[0028] Further, among metabolites in males, fumarate, asparagine
dihydrate, .beta.-alanine, L-cysteine and tyrosine tend to
increase, while stearic acid, succinate, palmitic acid, lactic acid
and glycine show a decreasing tendency.
[0029] Further, among the metabolites in females, succinate,
palmitic acid, lactate, stearic acid and glycine tend to increase,
while fumarate, asparagine dihydrate, .beta.-alanine, L-cysteine
and tyrosine show a decreasing tendency.
[0030] The increasing or decreasing tendency means an increase or
decrease in concentrations of metabolites, and the term "increased
metabolite concentration" means that the urine metabolite
concentration of male to female or the urine metabolite
concentration of female to male has increased significantly to be
measurable. Likewise, in this specification, the term "decreased
metabolite concentration" means that the urine metabolite
concentration of female to male or the urine metabolite
concentration of male to female has decreased significantly to be
measurable.
[0031] The quantification device included in the kit of the present
invention may be a chromatograph/mass spectrometer.
[0032] Chromatography used in the present invention may include,
for example, gas chromatography, liquid-solid chromatography (LSC),
paper chromatography (PC), thin-layer chromatography (TLC),
gas-solid chromatography (GSC), liquid-liquid chromatography (LLC),
foam chromatography (FC), emulsion chromatography (EC), gas-liquid
chromatography (GLC), ion chromatography (IC), gel filtration
chromatography (GFC), or gel permeation chromatography (GPC), but
it is not limited thereto. In fact, all quantitative chromatography
methods commonly used in the art may be used. Preferably, the
chromatography used in the present invention is gas
chromatography/time-of-flight mass spectrometry (GC/TOF MS).
[0033] With regard to the metabolite in the present invention, each
component is separated by gas chromatography, and constitutional
components thereof may be identified through structural information
(elemental composition) as well as accurate molecular weight
information using information obtained through TOF MS.
[0034] The present invention may also include a method for analysis
of metabolite differentiation in urine to distinguish different
groups.
[0035] According to one embodiment, the present invention may
provide a method for analysis of metabolite differentiation in a
urine sample to distinguish different groups (e.g., gender,
disease, etc.).
[0036] Specifically, there is provided a method for analyzing
differences of metabolites between different groups in urine
samples, including sampling a metabolite by extracting the
metabolite from a urine sample using pure methanol or a solvent
mixture of formic acid and methanol without urease treatment.
[0037] The analysis method of metabolite differentiation may be a
method of analyzing differentiation of metabolites in a urine
sample between different groups, which includes a metabolite
sampling step including: a quenching process; and a metabolite
extraction process.
[0038] The metabolite sampling process may include extracting
metabolites from the urine sample using pure methanol, pure
ethanol, a mixture of acetonitrile:water; a mixture of
water:2-propanol:methanol, or a mixture of formic acid:methanol
without urease treatment. Specifically, the mixed solvent of formic
acid:methanol is more preferably used. A mixing ratio of formic
acid and methanol is more preferably a volume ratio of
0.05-0.5:99.5-99.95.
[0039] In this regard, the urine and extraction solvent are
preferably treated in a volume ratio of 1:8 to 10 in order to
reduce error in experiments.
[0040] The metabolites extracted in the metabolite sampling step
may undergo the following analysis stages:
[0041] analyzing the extracted metabolites by means of a gas
chromatograph/time-of-flight mass spectrometer (GC/TOF MS);
[0042] converting the GC/TOF MS analysis result into a numerical
value capable of statistically processed; and
[0043] statistically verifying discrimination between different
groups using the converted value.
[0044] Next, in order to compare a profiling difference in
metabolites, partial least squares discriminant analysis (PLS-DA)
was conducted to select metabolite biomarkers showing significant
differences between different groups, so as to perform analysis and
verification.
[0045] According to an embodiment, with regard to the analysis
method of the present invention, the conversion of GC/TOF MS
analysis results into statistically processable values may include
dividing a total analysis time by unit time intervals, and
determining the largest one of an area or height of chromatogram
peaks displayed during the unit time as a representative value for
the unit time.
[0046] The statistical verification of discrimination between two
biological sample groups using the converted values may include
analyzing and verifying metabolite biomarkers showing a significant
difference between two biological sample groups through partial
least squares discriminant analysis (PLS-DA).
[0047] The metabolite biomarkers according to an embodiment of the
present invention may distinguish the gender of male and
female.
[0048] The metabolite biomarkers may include succinate, fumarate,
asparagine dihydrate, palmitic acid, .beta.-alanine, L-cysteine,
lactate, tyrosine, glycine and stearic acid.
[0049] A positive loading value of the partial least squares
discriminant analysis (PLS-DA) indicates an increase in metabolite
biomarkers, while a negative loading value indicates a decrease in
metabolite biomarkers.
[0050] According to an embodiment of the present invention,
biomarkers used herein for distinguishing gender may include one or
more selected from the group consisting of succinate, fumarate,
asparagine dihydrate, palmitic acid, .beta.-alanine, L-cysteine,
lactate, tyrosine, glycine and stearic acid.
[0051] Among the biomarkers, fumarate, asparagine dihydrate,
.beta.-alanine, L-cysteine and tyrosine tend to increase in males,
while succinate, palmitic acid, lactate, stearic acid and glycine
show a decreasing tendency in males.
[0052] On the other hand, among the biomarkers, succinate, palmitic
acid, lactate, stearic acid and glycine tend to increase in
females, while fumarate, asparagine dihydrate, .beta.-alanine,
L-cysteine and tyrosine show a decreasing tendency in females.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF INVENTION
[0053] Hereinafter, the present invention will be described in more
detail through examples according to the present invention, but the
scope of the present invention is not limited by the examples
presented below.
EXAMPLE
Example 1: Metabolite Profiling of 68 Urine Samples Using
PLS-DA
[0054] Urine samples obtained from 68 healthy adults (Table 1) were
divided and treated as follows: a stationary culture group at
37.degree. C. for 1 hour with urease treatment (UI); a stationary
culture group at 37.degree. C. for 1 hour without urease treatment
(WI); and a non-stationary culture without urease treatment (DE),
followed by extracting metabolites using pure methanol which has
been widely used as an extraction solvent and then GC/TOF MS
analysis.
[0055] 107 metabolites were identified in the chemical classes of
amines, amino acids, sugars and sugar alcohols, fatty acids and
organic acids (Table 2).
[0056] In order to compare the profiling differences in
metabolites, PLS-DA was conducted based on 106 metabolites except
urea. With regard to the urease treatment and stationary culture
group, the non-urease treatment and stationary culture treatment
group, and the non-urease treatment and non-stationary group,
respectively, different metabolite patterns were observed (FIG. 1,
Tables 3 and 4). In other words, the metabolite profile of the
urease treatment and stationary culture group was negative for most
samples in terms of t[1] and t[2] values in a score plot. Likewise,
the non-urease treatment and stationary culture group was positive
for most samples in terms of t[1] and t[2] values in the score
plot, while the non-urease treatment and non-stationary culture
group had positive t[1] values and negative t[2] values for most
samples. Briefly, it was confirmed that the metabolite profiles
were completely distinguished according to the treatment methods
(Table 3). Therefore, it could be demonstrated that the treatment
methods, such as urease treatment or stationary culture, may modify
or change other original metabolites in urine as well as urea.
[0057] Table 1 below shows urine sample information of 68
people.
[0058] Table 2 below shows 107 metabolites extracted from 68 urine
samples using pure methanol.
[0059] Table 3 below shows t[1](PC1) and t[2](PC2) values
represented as average and standard deviation (SD) in the
metabolite profiles between: the stationary culture group at
37.degree. C. for 1 hour with urease treatment using PLS-DA (UI);
the stationary culture group at 37.degree. C. for 1 hour with
non-urease treatment (WI); and the non-urease-treatment and
non-stationary culture group (DE).
[0060] Table 4 below shows loading values of the metabolites in the
metabolite profiles between: the stationary culture group at
37.degree. C. for 1 hour with urease treatment using PLS-DA (UI);
the stationary culture group at 37.degree. C. for 1 hour with
non-urease treatment (WI); and the non-urease-treatment and
non-stationary culture group (DE).
TABLE-US-00001 TABLE 1 Adult male sample Age Adult female sample
Age Male_l M133 Female_1 F/32 Male_2 M/32 Female_2 F/36 Male_3 M/32
Female_3 F/37 Male_4 M/37 Female_4 F/34 Male_5 M/36 Female_5 F/37
Male_6 M/32 Female_6 F/39 Male_7 M/38 Female_7 F/39 Male_8 M/37
Female_8 F/38 Male_9 M/39 Female_9 F/34 Male_10 M/37 Female_10 F/37
Male_11 M/30 Female_11 F/36 Male_12 M/34 Female_12 F/38 Male_13
M/35 Female_13 F/39 Male_14 M/41 Female_14 F/36 Male_15 M/41
Female_15 F/45 Male_16 M/42 Female_16 F/44 Male_17 M/49 Female_17
F/47 Male_18 M/41 Female_18 F/48 Male_19 M/48 Female_19 F/43
Male_20 M/44 Female_20 F/42 Male_21 M/46 Female_21 F/40 Male_22
M/43 Female_22 F/46 Male_23 M/42 Female_23 F/42 Male_24 M/41
Female_24 F/41 Male_25 M/48 Female_25 F/43 Male_26 M/50 Female_26
F/53 Male_27 M/54 Female_27 F/50 Male_28 M/51 Female_28 F/51
Male_29 M/52 Female_29 F/50 Male_30 M/51 Female_30 F/51 Male_31
M/53 Female_31 F/51 Female_32 F/54 Female_33 F/53 Female_34 F/52
Female_35 F/52 Female_36 F/65 Female_37 F/63
TABLE-US-00002 TABLE 2 Identification of metaboiltes Amines
2-hydroxypyridine 3-hydroxypyridine 5-deoxy-5- methylthioadenosine
adenosne benzamide carnitine glycocyamine hypoxanthine inosine
nicotinamide O-phosphorylethanolamine spermidine thymine tyrosine
uracil urea uric acid uridine xanthine Ammo acids alanine
asparagine dehydrated glycine histidine isoleucine L-allothreonine
L-cysteine L-homoserine lysine methionine methionine sulfoxide
N-methylalanine ornithine oxoproline phenylalanine proline serine
threonine tryptophan valine .beta.-alanine Fatty acids
1-monopalmitin 1-monostearin arachidic acid capric acid
heptadecanoic acid lignoceric acid myristic acid palatinitol
palmitic acid pelargonic acid stearic acid Organic acids
2-hydroxyvalerate 2-ketoadipate 3-hydroxypropionate 5-aminovalerate
adipate aspartate citramalate citrate DL-3-aminoisobutyrate
fumarate galactonate galacturonate gluconic acid lactone glycerate
glycolate guaiacol hexonate indole-3-lactate lactate lactobionate
malate malonate oxalate oxamate pyrrole-2-carboxylate pyruvate
succinate Sugars and sugar alcohols 1,5-anhydroglucitol
3,6-anhydro-D- galactose arabitol dihydoxyacetone fructose glycerol
galactinol galactose glucose glycerol-1-phosphate lactose lyxose
maltotriose mannitol mannose melibiose myo-inositol ribose sucrose
tagatose threitol threose trehalose xylose Miscellaneous
1,2,4-benzenetriol caffeic acid phosphate taurine xanthurenic
acid
TABLE-US-00003 TABLE 3 Class t[1]_average t[2]_average t[1]_stdev
t[2]_stdev DE 4.368 -0.401 2,117 1.687 WI 0.837 3231 3.376 3.703 UI
-5.257 -2.776 4.334 1.468
[0061] Table 3 shows that types and amounts of the metabolites may
vary depending upon treatment. It could be assumed that the
metabolites may be extracted from the DE group without any
pre-treatment, thereby maintaining the original types and amounts
of metabolites in urine. The urease treatment and stationary
culture group at 37.degree. C. for 1 hour (UI) and the non-urease
treatment and stationary culture group at 37.degree. C. for 1 hour
(WI) had changed t[1] values or t[2] values in most samples,
thereby demonstrating variation in types and amounts of the
metabolites (FIG. 1, Table 3). Changes in the type and amount of
metabolites through such treatment were found result in changes of
the type or amount of biomarker substances for diagnosis of
diseases, reduce the ability to discover biomarkers, and as a
result false biomarkers may be selected.
[0062] Therefore, since the urease treatment changes the metabolite
profile (Table 3), a biomarker discovering ability is lower than
that of the non-urease treatment group DE having intrinsic
metabolite profile.
TABLE-US-00004 TABLE 4 Metabolite Loading 1 Loading 2
1,2,4-benzenetriol 0.015 0.174 1,5-anhydroglucitol -0.043 0.020
1-monopalmitin 0.001 -0.013 1-monostearin -0.058 -0.047
2-hydroxypyridine 0.048 0.269 2-hydroxyvalerate -0.111 -0.025
2-ketoadipate 0.001 0.078 3,6-anhydro-D-galactose -0.102 0.052
3-hydroxypropionate -0.134 -0.067 3-hydroxypyridine -0.058 0.120
5-aminovalerate -0.055 0.037 5'-deoxy-5'-methylthioadenosine -0.130
-0.067 Adenosine -0.128 -0.004 Adipate -0.035 0.095 Alanine -0.085
0.067 arabitol -0.019 0.122 arachidic acid -0.080 0.072 asparagine
dehydrate -0.076 -0.010 aspartate -0.076 0.029 benzamide -0.031
0.134 O-alanine -0.043 0.035 caffeic acid -0.023 0.112 capric acid
-0.160 -0.135 carnitine 0.041 0.135 citramalate -0.098 0.035
citrate 0.009 0.080 dihydroxyacetone -0.002 0.064
DL-3-aminoisobutyrate 0.018 0.041 fructose -0.052 0.059 fumarate
0.037 0.246 galactinol -0.069 -0.007 galactonate -0.158 -0.032
galactose -0.093 0.090 galacturonate -0.159 -0.044 gluconic acid
lactone -0.084 0.025 glucose -0.111 0.057 glycerate -0.075 -0.032
glycerol -0.153 -0.080 glycerol-1-phosphate -0.043 0.129 glycine
0.048 0.136 glycocyamine -0.090 0.070 glycolate -0.130 -0.046
guaiacol 0.038 0.175 heptadecanoic acid -0.147 0.047 hexonate
-0.152 -0.096 histidine -0.104 -0.057 hypoxanthine -0.045 0.135
indole-3-lactate -0.075 0.026 inosine -0.080 -0.001 isoleucine
-0.165 -0.095 lactate -0.076 0.010 lactobionate -0.099 -0.090
lactose -0.218 -0.261 L-allothreonine -0.009 0.147 L-cysteine
-0.101 0.009 L-homoserine -0.143 -0.131 lignoceric acid -0.092
-0.041 lysine -0.066 0.014 lyxose -0.052 0.063 malate -0.060 0.076
malonate 0.032 0.108 maltotriose -0.071 -0.076 mannitol -0.026
0.036 mannose -0.047 0.134 melibiose -0.063 -0.008 methionine
-0.131 -0.065 methionine sulfoxide -0.132 0.026 myo-inositol -0.049
0.048 myristic acid -0.112 0.034 nicotinamide 0.008 0.211
N-methylalanine -0.137 -0.078 O-phosphorylethanolamine -0.129
-0.048 ornithine -0.083 0.015 oxalate -0.177 -0.128 oxamate 0.166
0.193 oxoproline -0.008 0.252 palatinitol -0.128 -0.080 palmitic
acid -0.164 0.019 pelargonic acid -0.127 -0.018 phenylalanine
-0.137 -0.030 phosphate -0.033 -0.019 proline -0.105 -0.128
pyrrole-2-carboxylate -0.074 -0.017 pyruvate -0.027 -0.051 ribose
-0.131 0.015 serine -0.187 -0.175 spermidine 0.006 0.126 stearic
acid -0.013 0.152 succinate -0.042 0.103 sucrose -0.166 -0.127
tagatose -0.052 0.045 taurine -0.087 -0.019 threitol -0.010 0.079
threonine 0.006 0.126 threose -0.096 -0.138 thymine -0.016 0.214
trehalose -0.207 -0.232 tryptophan -0.099 0.019 tyrosine -0.086
0.046 uracil -0.068 0.070 uric acid -0.037 0.077 uridine -0.096
0.036 valine -0.150 -0.051 xanthine -0.076 0.118 xanthurenic acid
-0.084 0.093 xylose -0.125 -0.033
Example 2: Selection of Major Metabolites in 68 Urine Samples
[0063] Using the PES-DA analysis from Example 1, the top 10 major
metabolites contributing greatly to classification of 68 urine
samples into three (3) groups, that is: a stationary culture group
at 37.degree. C. for 1 hour with urease treatment using PLS-DA
(UI); a stationary culture group at 37.degree. C. for 1 hour with
non-urease treatment (WI); and a non-urease-treatment and
non-stationary culture group (DE), were selected with reference to
VIP (variable importance in projection) score values (Table 5).
[0064] Table 5 below shows VIP score values of the 10 major
metabolites that have high differences in metabolite profiles
between: the stationary culture group at 37.degree. C. for 1 hour
with urease treatment using PLS-DA (UI); the stationary culture
group at 37.degree. C. for 1 hour with non-urease treatment (WI);
and the non-urease-treatment and non-stationary culture group
(DE).
TABLE-US-00005 TABLE 5 Metabolites VIP value Succinate 2.650
palmitic acid 2.468 1-monostearin 2.093 1-monopalmitin 1.873
Benzamide 1.786 heptadecanoic acid 1.724 Malate 1.696 O-alanine
1.632 Histidine 1.573 gluconic acid lactone 1.567
Example 3: Metabolite Profiling to Distinguish Male and Female of
68 Urine Samples Using PLS-DA
[0065] Among the urine samples obtained from 68 healthy adults
(Table 1), 31 male urine samples and 37 female urine samples were
extracted without urease treatment and metabolites were extracted
using pure methanol which has been previously used, as an
extraction solvent, followed by analysis through GC/TOF MS.
Thereafter, a PLS-DA model was prepared using 106 metabolites
excluding urea, so as to distinguish the gender (FIG. 2, Tables 6
and 7).
[0066] As shown in FIG. 2, metabolites in urine of males and
females have different patterns, and statistically significant
differences were shown based on the PLS-DA model. That is, the
metabolite profile for male classification was positive in the
score plot for most samples in terms of t[1] and t[2] values, and
the metabolite profile for female classification was negative in
the score plot for most samples in terms of [t]1 and t[2] values,
thereby demonstrating that the metabolite profiles in relation to
the gender were completely distinguished (Table 7). In order to
select the major metabolites showing a difference in metabolite
profiles, metabolites having the same trend in both loading 1 and
loading 2 in Table 8 were selected.
[0067] Table 6 below shows the average and standard deviation of
the t[1] and t[2] values of each sample in the metabolite profile
that shows a difference in metabolite profiling to distinguish
males and females from 68 urine samples using PLS-DA.
[0068] Table 7 below shows the loading values of each metabolite in
the metabolite profile that shows a difference in metabolite
profiling to distinguish males and females from 68 urine samples
using PLS-DA.
TABLE-US-00006 TABLE 6 Class t[1]_average t[2]_average t[1]_stdev
t[2]_stdev Male -3.0..54 -2.210 3.821 2485 Female 2.558 1.852 1.981
1231
TABLE-US-00007 TABLE 7 Metabolite Loading 1 Loading 2
1,2,4-benzenetriol 0.070 -0.043 1,5-anhydroglucitol -0.043 -0.120
1-monopalmitin 0.132 0.155 1-monostearin 0.140 0.161
2-hydroxypyridine 0.082 -0.028 2-hydroxyvalerate 0.047 -0.038
2-ketoadipate 0.047 0.105 3,6-anhydro-D-galactose 0.110 -0.011
3-hydroxypropionate -0.068 -0.229 3-hydroxypyridine 0.103 0.012
5-aminovalerate 0.055 -0.034 5'-deoxy-5'-methylthioadenosine -0.015
-0.123 adenosine 0.111 0.004 Adipate 0.009 -0.074 Alanine 0.119
0.031 arabitol 0.122 0.071 arachidic acid 0.046 -0.030 asparagine
dehydrate 0.210 0.139 aspartate -0.022 -0.195 benzamide 0.023
-0.075 O-alanine 0.195 0.149 caffeic acid -0.015 -0.073 capric acid
0.116 0.058 camitine 0.021 0.060 citramalate 0.091 -0.019 Citrate
-0.111 -0.236 dihydroxyacetone 0.039 0.089 DL-3-aminoisobutyrate
0.079 0.019 fructose 0.006 -0.151 fumarate 0.250 0.231 galactinol
-0.040 -0.102 galactonate 0.015 -0.149 galactose 0.110 -0.030
galacturonate 0.094 -0.019 gluconic acid lactone 0.064 -0.038
Glucose 0.073 -0.083 glycerate -0.022 -0.096 glycerol -0.056 -0.228
glycerol-1-phosphate 0.101 0.044 Glycine -0.132 -0.242 glycocyamine
0.019 -0.146 glycolate 0.074 -0.043 guaiacol 0.040 0.053
heptadecanoic acid -0.045 -0.203 hexonate 0.060 0.010 histidine
0.138 0.059 hypoxanthine 0.134 0.041 indole-3-lactate -0.047 -0.124
Inosine -0.020 -0.095 isoleucine 0.164 0.096 Lactate -0.119 -0.262
lactobionate -0.070 -0.219 Lactose -0.057 -0.153 L-allothreonine
0.104 0.128 L-cysteine 0.192 0.102 L-homoserine 0.044 -0.046
lignoceric acid 0.100 0.081 Lysine 0.098 0.031 Lyxose 0.082 0.034
Malate -0.093 -0.227 malonate 0.125 0.088 maltotriose -0.054 -0.071
mannitol 0.126 0.112 Mannose 0.048 -0.088 melibiose -0.041 -0.092
methionine 0.157 0.057 methionine sulfoxide 0.117 -0.028
myo-inositol 0.048 -0.023 myristic acid -0.034 -0.152 nicotinamide
0.131 0.012 N-methylalanine 0.128 0.068 O-phosphorylethanolamine
0.096 0.049 ornithine -0.009 -0.113 Oxalate 0.036 -0.008 Oxamate
0.066 0.051 oxoproline 0.114 -0.034 palatinitol 0.015 -0.052
palmitic acid -0.086 -0.281 pelargonic acid -0.096 -0.220
phenylalanine 0.136 0.001 phosphate -0.026 -0.094 Proline 0.105
0.232 pyrrole-2-carboxylate 0.049 -0.026 pyruvate 0.023 -0.055
Ribose 0.064 -0.071 Serine 0.107 0.029 spermidine 0.088 0.027
stearic acid -0.107 -0.242 succinate -0.180 -0.353 Sucrose -0.034
-0.069 tagatose 0.023 -0.079 Taurine 0.051 -0.025 threitol 0.068
-0.083 threonine 0.107 0.193 Threose 0.016 -0.031 Thymine 0.153
0.075 trehalose -0.018 -0.078 tryptophan 0.154 0.049 tyrosine 0.178
0.088 Uracil -0.007 -0.126 uric acid 0.146 0.131 Uridine 0.135
-0.002 Valine 0.115 0.012 xanthine 0.017 -0.088 xanthurenic acid
0.143 0.034 Xylose -0.026 -0.162
Example 4: Selection of Major Metabolites Showing Differences in
Metabolite Profiling that Distinguishes Males and Females from 68
Urine Samples Using PLS-DA
[0069] Using the PLS-DA analysis from Example 3, it was confirmed
that each gender group was separated, and the top to major
metabolites showing high VIP values, which are a degree of
contribution to the separation of gender in the model, were
selected. (Table 8). Further, the amounts of 10 major metabolites
were indicated in a box plot to compare the same with the amounts
of metabolites according to gender (FIG. 3).
[0070] Next, Table 8 below shows VIP (variable importance in
projection) score values of the 10 major metabolites having have
significant differences in metabolite profiles that show a
difference in metabolite profiling to distinguish males and females
from 68 urine samples using PLS-DA.
TABLE-US-00008 TABLE 8 Metabolite VIP score succinate 2.045
fumarate 2.003 asparagine dehydrate 1.666 palmitic acid 1.595
O-alanine 1.541 L-cysteine 1.540 lactate 1.494 tyrosine 1.432
glycine 1.420 stearic acid 1.373
Example 5: Selection of the Optimal Extraction Solvent for Analysis
of Metabolites in Urine Samples
[0071] In order to obtain metabolite samples from urine samples, 68
urine samples were combined in equal proportions to form a urine
mixture, and then, 100 .mu.l of the urine mixture was directly
treated with 900 .mu.l of extraction solvent, that is: pure
methanol (MeOH); pure ethanol (EtOH); a mixture of
acetonitrile:water (50 ACN; 1:1, v/v); a mixture of
water:2-propanol/methanol (WiPM; 2:2:5, v/v/v); and a mixture of
formic acid:methanol (AM; 0.125:99.875, v/v), respectively, without
urease treatment, so as to extract metabolites, followed by
GC/TOF-MS analysis to compare and analyze extraction efficiencies
thereof.
[0072] In the urine mixture, 113 metabolites including amines,
amino acids, sugars and sugar alcohols, fatty acids, and organic
acids were identified (Table 9).
[0073] As shown in FIGS. 4 and 5, it was confirmed that the
extraction rate and extraction reproducibility were different
depending on the extraction solvent. It could be seen that the peak
intensity analyzed qualitatively and relatively quantitatively was
the highest in AM, thereby demonstrating the highest extraction
rate of comprehensive metabolites in AM (FIG. 4). Further, with
regard to reproducibility according to the extraction solvent, it
was found that the % CV value recorded the lowest value in both AM,
thereby demonstrating the highest reproducibility (FIG. 5).
Further, the protein sedimentation rate recorded the second highest
value in AM, thereby demonstrating appropriate protein
sedimentation ability of AM (FIG. 6). According to the above
results. AM was selected as the optimal solvent based on the
extraction rate, reproducibility and protein precipitation rate
when metabolites are extracted for metabolite analysis in
urine.
[0074] Table 9 below shows 113 metabolites extracted from a human
urine mixture sample using: pure methanol; pure ethanol; a mixture
of acetonitrile:water; a mixture of water:2-propanol:methanol; and
a mixture of formic acid:methanol, respectively.
TABLE-US-00009 TABLE 9 Identification of metaboiltes Amines
2-hydroxypyridine 3-hydroxypyridine 5-deoxy-5- methylthioadenosine
adenosne benzamide carnitine glycocyamine hypoxanthine inosine
nicotinamide O-phosphorylethanolamine spermidine thymine tyrosine
uracil urea uric acid uridine xanthine Ammo acids alanine
asparagine dehydrated glycine histidine isoleucine L-allothreonine
L-cysteine L-homoserine lysine methionine methionine sulfoxide
N-methylalanine ornithine oxoproline phenylalanine proline serine
threonine tryptophan valine .beta.-alanine Fatty acids
1-monopalmitin 1-monostearin arachidic acid capric acid
heptadecanoic acid lignoceric acid myristic acid palatinitol
palmitic acid pelargonic acid stearic acid Organic acids
2-hydroxyvalerate 2-ketoadipate 3-hydroxypropionate 5-aminovalerate
adipate aspartate citramalate citrate DL-3-aminoisobutyrate
fumarate galactonate galacturonate gluconic acid lactone glycerate
glycolate guaiacol hexonate indole-3-lactate lactate lactobionate
malate malonate oxalate oxamate pyrrole-2-carboxylate pyruvate
succinate Sugars and sugar alcohols 1,5-anhydroglucitol
3,6-anhydro-D- galactose arabitol dihydoxyacetone fructose glycerol
galactinol galactose glucose glycerol-1-phosphate lactose lyxose
maltotriose mannitol mannose melibiose myo-inositol ribose sucrose
tagatose threitol threose trehalose xylose Miscellaneous
1,2,4-benzenetriol caffeic acid phosphate taurine xanthurenic
acid
* * * * *